Summary of Work: PREDICTIVE-TOXICOLOGY EVALUATION PROJECT: The second NIEHS Predictive-Toxicology Evaluation involves 30 NTP chemobioassays for carcinogenesis. Thus far it has generated 18 sets of predictions from 13 groups in 4 countries; 14 manuscripts were published together in an EHP Supplement. MODELS: Human- expert heuristic and the following six intelligent- computer-system models are under development: decision tree by induction, rule set from decision trees, back-propagation neural network, rule set from trained neural net, Bayesian-belief net, and inductive- logic programming. Each uses a fundamentally different approach to perform pattern- recognition analysis of learning sets, to identify specific biological and chemical features and relationships that may augment human-hypothesis formation about mechanistic pathways of chemotoxicity. The multiple-model/common training-set approach creates an ideal opportunity to evaluate model differences and by consensus analysis, identify and combine unique aspects of many models to provide one that predicts with greater confidence and perhaps greater accuracy. DATABASE COMPILATION AND REPRESENTATION: This is an ongoing activity, because opportunities and success of the database-mining research approach are limited only by the availability of enough data of suitable quality. We compiled values on the following chemical attributes: sub- structural alert and de-alert, SMILES code, 2-D structure, molecular weight, logP, highest-occupied and lowest-unoccupied molecular-orbital energies (HOMO & LUMO), and COMPACT ratio. Dose measures were converted to molar units. Representations that utilize specific morphology, in addition to information about presence or absence of any lesion at a site, were developed.